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N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma

Background: The role of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in osteosarcoma (OS) has not been fully studied yet. We aimed to identify m6A-related lncRNAs that could act as prognostic biomarkers for OS. Methods: Pearson correlation was performed to identify m6A-related lnc...

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Autores principales: Yang, Kun, Wang, Fengyan, Li, Ke, Peng, Guoxuan, Yang, Hua, Xu, Hong, Xiang, Yang, Sun, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019337/
https://www.ncbi.nlm.nih.gov/pubmed/35422168
http://dx.doi.org/10.1177/15330338221085354
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author Yang, Kun
Wang, Fengyan
Li, Ke
Peng, Guoxuan
Yang, Hua
Xu, Hong
Xiang, Yang
Sun, Hong
author_facet Yang, Kun
Wang, Fengyan
Li, Ke
Peng, Guoxuan
Yang, Hua
Xu, Hong
Xiang, Yang
Sun, Hong
author_sort Yang, Kun
collection PubMed
description Background: The role of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in osteosarcoma (OS) has not been fully studied yet. We aimed to identify m6A-related lncRNAs that could act as prognostic biomarkers for OS. Methods: Pearson correlation was performed to identify m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct the risk model and assess whether the risk score was an independent prognostic factor for patients with OS. Gene Set Enrichment Analysis (GSEA) was performed to analyze the functions of genes in high-risk and low-risk groups. StarBase and Cytoscape were used to construct a competing endogenous RNA (ceRNA) network based on m6A-related prognostic lncRNA signature. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of genes involved in the ceRNA network. Results: We extracted 122 common lncRNAs from TCGA and Gene Expression Omnibus (GEO) databases. Pearson correlation results revealed 59 significant m6A-related lncRNAs in The Cancer Genome Atlas (TCGA) database, from which 2 were screened to construct a risk signature in TCGA dataset, which was then validated in the GEO dataset. A corresponding risk score was calculated and shown to be an independent prognostic factor for patients with OS. Enrichment analysis indicated that cell proliferation-related biological processes were more common in the high-risk group, while immune-related biological processes were more common in the low-risk group. Moreover, we established a nomogram that had a good ability to predict the overall survival of patients with OS. Additionally, a ceRNA network based on small nucleolar RNA host gene 7 (SNHG7) and small nucleolar RNA host gene 12 (SNHG12) was constructed, with genes that were enriched in hepatocellular carcinoma, gastric cancer, and non-small-cell lung cancer pathways. Conclusion: Our study revealed the prognostic role of m6A-related lncRNAs in OS and identified SNHG7 and SNHG12 as potential biomarkers for predicting the prognosis of patients with OS. These findings have enriched our understanding of the role of m6A modification in the dysregulation of lncRNAs in OS.
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spelling pubmed-90193372022-04-21 N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma Yang, Kun Wang, Fengyan Li, Ke Peng, Guoxuan Yang, Hua Xu, Hong Xiang, Yang Sun, Hong Technol Cancer Res Treat Original Article Background: The role of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in osteosarcoma (OS) has not been fully studied yet. We aimed to identify m6A-related lncRNAs that could act as prognostic biomarkers for OS. Methods: Pearson correlation was performed to identify m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct the risk model and assess whether the risk score was an independent prognostic factor for patients with OS. Gene Set Enrichment Analysis (GSEA) was performed to analyze the functions of genes in high-risk and low-risk groups. StarBase and Cytoscape were used to construct a competing endogenous RNA (ceRNA) network based on m6A-related prognostic lncRNA signature. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of genes involved in the ceRNA network. Results: We extracted 122 common lncRNAs from TCGA and Gene Expression Omnibus (GEO) databases. Pearson correlation results revealed 59 significant m6A-related lncRNAs in The Cancer Genome Atlas (TCGA) database, from which 2 were screened to construct a risk signature in TCGA dataset, which was then validated in the GEO dataset. A corresponding risk score was calculated and shown to be an independent prognostic factor for patients with OS. Enrichment analysis indicated that cell proliferation-related biological processes were more common in the high-risk group, while immune-related biological processes were more common in the low-risk group. Moreover, we established a nomogram that had a good ability to predict the overall survival of patients with OS. Additionally, a ceRNA network based on small nucleolar RNA host gene 7 (SNHG7) and small nucleolar RNA host gene 12 (SNHG12) was constructed, with genes that were enriched in hepatocellular carcinoma, gastric cancer, and non-small-cell lung cancer pathways. Conclusion: Our study revealed the prognostic role of m6A-related lncRNAs in OS and identified SNHG7 and SNHG12 as potential biomarkers for predicting the prognosis of patients with OS. These findings have enriched our understanding of the role of m6A modification in the dysregulation of lncRNAs in OS. SAGE Publications 2022-04-14 /pmc/articles/PMC9019337/ /pubmed/35422168 http://dx.doi.org/10.1177/15330338221085354 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Yang, Kun
Wang, Fengyan
Li, Ke
Peng, Guoxuan
Yang, Hua
Xu, Hong
Xiang, Yang
Sun, Hong
N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma
title N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma
title_full N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma
title_fullStr N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma
title_full_unstemmed N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma
title_short N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma
title_sort n6-methyladenosine modification-related long non-coding rnas are potential biomarkers for predicting the prognosis of patients with osteosarcoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019337/
https://www.ncbi.nlm.nih.gov/pubmed/35422168
http://dx.doi.org/10.1177/15330338221085354
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